RoboTwin 2.0 Leaderboard

Benchmark Setting

Training data is fixed to 50 demo_clean trajectories × 50 tasks (2,500 demos total) on Aloha-AgileX, then evaluated 100 trials/task under demo_clean and demo_randomized. Use the ranking control to switch titles between Average(c2c+c2r), clean2random(hard), and clean2clean(easy). Co-train and Single-task SFT share one board. The default ranking is Average(c2c+c2r).

Submission & listing policy

To be listed on this leaderboard, a model must provide publicly released code, publicly released weights, and a technical report (arXiv paper or equivalent public document) describing the method. The code must include post-training and deployment. If the result is fine-tuned from a pretrained model, the pretrained weights must be released as well, so supervised fine-tuning can be rerun from that initialization. To submit results or request listing, contact chentianxing2002@gmail.com with links to the code, weights, and report.

XPolicyLab

A Unified Standard and Open Ecosystem for Robot Policy Evaluation and Deployment

Institutions MMLab@HKU & THU
Contact Tianxing Chen (project lead), chentianxing2002@gmail.com

The same adapters serve this RoboTwin 2.0 board and RoboDojo (sim + real). Results here are reproduced via the XPolicyLab standard interface.

Rank by
Depth input
View
Ranked by Average(c2c+c2r) mean ↓
Current ranking setting

Average(c2c+c2r)

Train: 50 demo_clean × 50 tasks (2,500) Eval / sort: (c2c + c2r) / 2 Robot: Aloha-AgileX Track: Co-train (+ Single SFT baselines)

Overall Ranking — Average(c2c+c2r)

Latest update:
Rank Method Depth Contributor Average Clean → Random Clean → Clean Track Date

Depth: ✓ requires observed depth or depth-derived point clouds at inference; ✗ does not. Depth estimated from RGB and training-only depth supervision are excluded.

Per-task Results

Columns follow the current Average(c2c+c2r) / clean2random(hard) / clean2clean(easy) ranking. The depth filter also applies here. Methods tagged Single are single-task SFT baselines.

RoboTwin XPolicyLab RoboDojo

XPolicyLab bridges RoboTwin and RoboDojo with one policy interface, data format, and train/eval pipeline. Website · Code

Sim Leaderboard Top 10 · Ranked by Average · Score ↓

Dimension
Rank by

XPolicyLab bridges RoboTwin and RoboDojo: one policy interface, one data format, and one train/eval pipeline. Models listed here can be transferred to RoboDojo (sim + real) without rewriting a line of code. Preview below shows the official Top 10; cells are Score / SR% per capability dimension.

Show full leaderboard on robodojo-benchmark.com

Real Leaderboard Top 10 · Ranked by Average · Score ↓

Embodiment
Rank by

Official RoboDojo real-robot preview (Top 10). Cells are Score / SR% overall and on ARX X5, Piper, and Piper X.

Show full leaderboard on robodojo-benchmark.com

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